The incidence and risk factors for early death among patients with oral tongue squamous cell carcinomas

Guan, Xiyin; Li, Yujiao; Hu, Chaosu

doi:10.1111/ijcp.14352

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…Muhammad Faisal et al have shown that lymph node positivity, depth of invasion (DOI), and higher nodal ratio (LNR) were significant prognostic factors affecting OS in patients with OTSCC [ 15 ]. The study by Xiyin Guan et al has shown significant associations of advanced age,advanced stage, N-stage, distant metastasis, and absence of surgery with all-cause and cancer-specific early mortality in patients with OTSCC [ 16 ]. Additionally, several studies have shown that serum inflammatory markers, such as LMR, NLR, and CRP, can be used as independent prognostic indicators to predict survival in OTSCC patients [ 17 – 20 ].…”

Section: Discussionmentioning

confidence: 99%

Prediction of 5-year overall survival of tongue cancer based machine learning

Cheng

Jin

et al. 2023

BMC Oral Health

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Objective We aimed to develop a 5-year overall survival prediction model for patients with oral tongue squamous cell carcinoma based on machine learning methods. Subjects and methods The data were obtained from electronic medical records of 224 OTSCC patients at the PLA General Hospital. A five-year overall survival prediction model was constructed using logistic regression, Support Vector Machines, Decision Tree, Random Forest, Extreme Gradient Boosting, and Light Gradient Boosting Machine. Model performance was evaluated according to the area under the curve (AUC) of the receiver operating characteristic curve. The output of the optimal model was explained using the Python package (SHapley Additive exPlanations, SHAP). Results After passing through the grid search and secondary modeling, the Light Gradient Boosting Machine was the best prediction model (AUC = 0.860). As explained by SHapley Additive exPlanations, N-stage, age, systemic inflammation response index, positive lymph nodes, plasma fibrinogen, lymphocyte-to-monocyte ratio, neutrophil percentage, and T-stage could perform a 5-year overall survival prediction for OTSCC. The 5-year survival rate was 42%. Conclusion The Light Gradient Boosting Machine prediction model predicted 5-year overall survival in OTSCC patients, and this predictive tool has potential prognostic implications for patients with OTSCC.

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Section: Discussionmentioning

confidence: 99%

Prediction of 5-year overall survival of tongue cancer based machine learning

Cheng

Jin

et al. 2023

BMC Oral Health

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Early Death Incidence and Prediction Among Patients With Hypopharynx Squamous Cell Carcinomas

2023

Ear Nose Throat J

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Background: The objective of this study is to evaluate the incidence and associated factors for early death (ED) in hypopharynx squamous cell carcinomas (SCC) patients. Materials and Methods: Patients were extracted from the Surveillance, Epidemiology and End Results database between 2004 and 2014. The ED (survival time ≤3 months) rate was calculated, and associated risk factors were evaluated by the logistic regression models. Results: A total of 2659 patients were analyzed and 307 (11.5%) patients died within 3 months after cancer diagnosis, among whom 243 (79.2%) patients died from cancer-specific cause. In univariate analyses, advanced age, divorced/single/widowed (DSW), non-Caucasian, advanced T classification, distant metastasis, and no surgery were significantly associated with ED ( P < .05, respectively). Multivariate analyses showed that advanced age, DSW, advanced T classification, distant metastasis, and no surgery were significantly associated with all-cause and cancer-specific ED. Conclusion: Our results showed that a total of 11.5% patients with hypopharynx SCC suffered ED, among whom 79.2% patients died from cancer-specific cause. Predictors of ED are primarily related to age ≥62 years, advanced T classification, distant metastasis, and no surgery but also include unmarried status; better prognostic and predictive tools for select ED patients in larger sample size are needed.

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Prediction of 5-year overall survival of tongue cancer based machine learning

Cheng

et al. 2023

Preprint

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Objective We aimed to develop a 5-year overall survival prediction model for patients with oral tongue squamous cell carcinoma based on machine learning methods. Subjects and Methods: The data were obtained from electronic medical records of 224 OTSCC patients at the PLA General Hospital. A five-year overall survival prediction model was constructed using logistic regression, Support Vector Machines, Decision Tree, Random Forest, Extreme Gradient Boosting, and Light Gradient Boosting Machine. Model performance was evaluated according to the area under the curve (AUC) of the receiver operating characteristic curve. The output of the optimal model was explained using the Python package (SHapley Additive exPlanations, SHAP). Results After passing through the grid search and secondary modeling, the Light Gradient Boosting Machine was the best prediction model (AUC = 0.860). As explained by SHapley Additive exPlanations, N-stage, age, systemic inflammation response index, positive lymph nodes, plasma fibrinogen, lymphocyte-to-monocyte ratio, neutrophil percentage, and T-stage could perform a 5-year overall survival prediction for OTSCC. The 5-year survival rate was 42%. Conclusion The Light Gradient Boosting Machine prediction model predicted 5-year overall survival in OTSCC patients, and this predictive tool has potential prognostic implications for patients with OTSCC.

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The incidence and risk factors for early death among patients with oral tongue squamous cell carcinomas

Cited by 3 publications

References 30 publications

Prediction of 5-year overall survival of tongue cancer based machine learning

Prediction of 5-year overall survival of tongue cancer based machine learning

Early Death Incidence and Prediction Among Patients With Hypopharynx Squamous Cell Carcinomas

Prediction of 5-year overall survival of tongue cancer based machine learning

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